Paper Information

Foodborne listeriosis can cause stillbirth in pregnant individuals, prompting numerous population-specific food safety guidelines. Dose-response models are employed in quantitative microbial risk assessments (QMRAs) to develop such guidelines; however, pregnant hosts are under-considered when developing dose-response models, and the few published dose-response models for pregnant hosts do not consider the biological plausibility of the model. Therefore, this study aims to develop biologically plausible dose-response models for pregnant hosts using infection and stillbirth as health outcomes. The inclusion criteria of dose-response data for this analysis required reporting of (i) quantified dose, (ii) Listeria monocytogenes as the target pathogen, (iii) infection and/or stillbirth as endpoints, and (iv) specificity to pregnant hosts. The datasets were fit individually and as pooled sets to exponential and beta-Poisson dose-response models using Maximum Likelihood Estimation (MLE). To establish uncertainty around the MLE estimates, 10,000 bootstrapped iterations were fit to the models. Recommended dose-response models for endpoints of stillbirth and infection were generated. The recommended model for infection was based on pooled guinea pig and gerbil maternal infection data fit to the beta-Poisson model, with parameters of: α = 0.0843 and N50 = 1.01 × 108. The recommended stillbirth dose-response model utilized a pooled guinea pig fetal brain infection and stillbirth dataset fit to the beta-Poisson model parameters of α = 0.575 and N50 = 1.31 × 107. Integrating such models into QMRAs for pregnant hosts supports a more health-protective assessment and decision-making process by separating sensitive populations from the general public when possible.